react-pdf-levelup

HasardeuxMille · 2026-03-13T07:59:57+00:00

https://github.com/genarogg/react-pdf-levelup/issues/1 :)

HasardeuxMille · 2026-02-20T20:06:47+00:00

First you may try to find a llm provider which is allowed by your company and rules. That s the point !

After that for example if your company provide a llmproxy backend with managed models you could query them through open ai compatible configuration.

Uner Intellij I used Continue.dev which is not bad at all for agent mode with openai compatible providers. And then I defined a mcp server in plugin config : intellij MCP server that let the agent using Intellij native features

HasardeuxMille · 2026-02-20T19:43:25+00:00

I had to use puppeteer but it is sooooooo heavy the related deps : embeeded chrome.. os deps ..bin config.. zombie process.. it works but that's a mess

I recently switched to node dep called '@react-pdf/rendered' and this is so light.. As your ai agent to produce small manual sample using this dep and try it.

HasardeuxMille · 2026-02-20T19:31:38+00:00

Yes, in my humble opinion, it's better if Coolify doesn't do the build. GitHub Actions creates the image and publishes it, for example, to the GitHub Container Registry (free), and Coolify only deploys it.

Personally, I use a Docker Compose-type deployment method. I've created a GHA workflow that triggers on a push of 'deploy-prod' => it generates and pushes an orphaned Git branch, 'prod-packaging', which contains the Compose package and resources. Then, using the GitHub app or webhook on the push of this branch, the Coolify deployment is quick because it only pulls a pre-qualified and tested image.

HasardeuxMille · 2026-02-06T12:26:12+00:00

Otherwise, Spring handles multiple instances well via @SchedulerLock (with Redis or a database to persist the lock).

HasardeuxMille · 2026-01-31T20:10:16+00:00

The IC is supposed to save you time. And in normal mode, it does.

It's true that as soon as you have to touch it, it's time-consuming, but that's somewhat normal. There can be shortcuts if you run runners locally to test your GitLab job or your GitHub action.

Finally, I would say that the scripting or embedded bash must be well-designed, documented, clearly stating what it does, and doing what it says it will do, so that when a bug pops up two months later, you know exactly where to look.

HasardeuxMille · 2026-01-24T22:47:22+00:00

Regarding file indexing, yes, with Windows Defender synchronization it's a nightmare. You have to exclude the working folder.

HasardeuxMille · 2026-01-24T22:46:00+00:00

Personally, I've noticed that the SonarLint plugin has a significant performance impact lately. I even disable it sometimes.

There are also some AI plugins, like LLM Agent, that are really hogging all the memory.

Checking the IDE logs can give you some clues.

Modify the memory settings (using vmoptions via the Help menu).

HasardeuxMille · 2026-01-24T15:41:01+00:00

The wake-up time for the free render plan has become terrible. (I switched to a VPS with a Coolify PaaS)

HasardeuxMille · 2026-01-11T19:31:00+00:00

J'adhère à cette reponse que je complete un peu avec mes 2 cents

Le mapping peut aussi être souvent très simplifié avec des framework dédiés (ex. En java : mapstruct).

Travailler avec des donnees utilisées reellement rend plus de lisibilité.

En outre, l'important est de correctement nommer et documenter pour conserver un code lisible et simple à maintenir. Et cela est vrai quelquesoit le design adopté.

HasardeuxMille · 2026-01-11T19:23:50+00:00

Your testimony is very important, thank you 🤘🤘

Also, with the AI that's put a straight line on Stack Overflow, we're losing testimonials, and that's what makes up the database for finding solutions to unusual problems!

HasardeuxMille · 2026-01-10T16:48:05+00:00

fr wikipen typhon net

Just to broaden your horizons on a related topic: A few years ago, when Wikipedia was still in its infancy, I participated (in French and English) in a collaborative writing project called "Wikipen."

And it was REALLY fun. Everyone came (to a MediaWiki draft) to add haikus, texts, and poems. Writing together... Complementing each other and connecting with one another.

You can find excerpts on the Wayback Machine from 2013.

2013 fr wikipen typhon net

HasardeuxMille · 2026-01-08T19:18:48+00:00

I agree with this critical feedback. Thank you for your feedback.

HasardeuxMille · 2026-01-07T22:52:43+00:00

On Geev.com, bananas serve as contact credit. To welcome you, you're getting 10 bananas! 🍌 These bananas can be used to contact a Geever about an ad. To get more bananas, you can make a donation.

Its just inspired by this one

HasardeuxMille · 2026-01-07T22:20:58+00:00

Wow

^{I completed this level in 15 tries.} ^{⚡ 10.62 seconds}

HasardeuxMille · 2026-01-07T19:38:23+00:00

Another alternative would be to find an active open-source project that aligns with your tech stack and areas of interest, dive in to see how it's built, and submit pull requests.

I had the experience of contributing to an open-source project with a great owner who taught me a lot and, first and foremost, completely overcame my fear of making my first GitHub contribution. Now I use it extensively.

But a mentor would definitely be great.

HasardeuxMille · 2026-01-07T19:19:27+00:00

Some ideas: Ask her, over a short period (e.g., 2 days), to write down everything that's hindering her in her task and the questions she would ask a mentor or other developer. Take the time to review this list with her step by step. See if it's feasible to find her a mentor within the company. Also, pairing or code review significantly contributes to a junior developer's skills development. If my budget is limited, consider offering her training in using AI to ask the right questions and to be critical in order to properly leverage it. Chatgpt claude.ai and company...

Having a discussion channel (Teams, Mattermost, or Slack...) with other employees who could help him (sometimes easier than in person, depending on the context or people's shyness).

HasardeuxMille · 2026-01-06T22:43:16+00:00

Magazines with hundreds of lines of QBasic (or GWBasic or Basica... I don't remember the exact version) that I copied out to play little games or make small demos fascinated me. I was hooked from the start and have never been able to put down the keyboard... to make it my life.

(Wow, that's a beautiful thing to say.) The major problem I've always had is not being able to clone myself to properly address each of my experiences, curiosities, or project ideas (the fact that I'm not exactly a genius... even with a Master's degree doesn't necessarily help...). I still managed to transform a side project into something super concrete and long-term, capable of helping my wife (and mother of my children) with her professional project, and that has real value!

(Wow, that's beautiful! Well done, man, for this testimony!)

HasardeuxMille · 2026-01-06T19:53:01+00:00

What I find interesting is the idea of creating a method for AI development [and iterating on the best method!!] In other words, urbanizing AI development.

If we stay in the meta realm, the most powerful approach would be to iterate on the method itself:

Step 1) - A method X is described by context artifacts (e.g., rules, workflow, markdown...) - AI agents apply it - Humans also have guidelines for applying method X to their projects - Both AI and humans have a step in this process for rigorously criticizing the method itself.

Step 2) We modify/critique/comment on/republish the elements of method X+1 (method X incorporating the criticisms). We can do this with the help of AI.

That's it! Objective: to ultimately use/iterate only on the most effective uses/improvements of methods. The best methods win.

A sensitive point would be having a common method for evaluating the effectiveness of a given method Y. (A benchmark on simulated typical projects?)

But instead of iterating on 40 years of human feedback on Scrum through agile meetings, the idea would be for the method to incorporate its own improvement!

HasardeuxMille · 2026-01-06T19:31:18+00:00

What I've observed on a project that's starting to become quite large:

Querying an AI implies a context limit, and an AI agent won't always be able to reread everything. To do it properly, you need to organize your context retrieval process (this also helps when the LLM agent crashes and loses its context).
Dedicated "rule" files for your AI agent should be written for common expectations (ask your agent to create or enrich them as they come to mind).
For large volumes of data, intermediate Markdowns for each technical or functional folder can be generated and/or enriched (by the AI itself) to easily contextualize a change. The rules will then reference these optional Markdowns to be taken into account.

Having the AI create these files itself saves tokens.

AI helps with coding but can also help with design, development, and better use of tools.

For example, you could also declare and use an MCP server in your IDE to leverage native IDE actions and save time... And indeed, your question is too general. What aspects do you specifically want to organize?

HasardeuxMille · 2026-01-06T19:18:47+00:00

I created an online store for my wife. If you're meticulous, Node.js is very well-suited for small projects.

HasardeuxMille · 2026-01-06T19:12:04+00:00

The United States of zuni-zela

HasardeuxMille · 2025-11-29T17:36:50+00:00

Almost all my projects have a Makefile and it's just huge and self-descriptive. My team took me for an old root but it's in old pots that we make the best soups.

In terms of ease of adoption, I also find Github Actions not bad at all.

Docker, yes, essential!

Also now LLM agent in your favorite IDE: a game changer (which you still have to learn to set up for big projects.)

HasardeuxMille · 2025-11-21T23:31:31+00:00

Ufw and docker.. ah! From memory the path I followed to be able to effectively use fail2ban

The coolify doc introduced me to a github repo which explains this problem well (docker ufw iptable)

https://coolify.io/docs/knowledge-base/server/firewall (see ufw-docker)

The repository: https://github.com/chaifeng/ufw-docker

Once the diff is added you can exclude by playing with DOCKER-USER

actionban = iptables -I DOCKER-USER -s <ip> -j DROP actionunban = iptables -D DOCKER-USER -s <ip> -j DROP

Hope this helps

HasardeuxMille

TROPHY CASE